Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	113,047
2	South Dakota	99,499
3	Iowa	79,326
4	Wisconsin	77,661
5	Nebraska	74,486
6	Utah	69,416
7	Montana	65,674
8	Wyoming	65,459
9	Idaho	65,289
10	Minnesota	64,503
11	Rhode Island	64,471
12	Illinois	64,236
13	Kansas	62,953
14	Tennessee	60,482
15	Indiana	59,596
16	Arkansas	58,536
17	Nevada	57,358
18	Mississippi	57,346
19	Alabama	57,143
20	Missouri	56,871
21	Oklahoma	56,354
22	Louisiana	55,694
23	New Mexico	53,867
24	Arizona	52,789
25	Alaska	52,642
26	Florida	50,440
27	Texas	47,815
28	Kentucky	47,675
29	Colorado	47,641
30	Georgia	47,026
31	South Carolina	46,505
32	Michigan	44,840
33	Ohio	44,495
34	Delaware	43,535
35	New Jersey	43,159
36	Connecticut	39,421
37	North Carolina	39,296
38	Massachusetts	39,087
39	New York	37,985
40	Maryland	36,881
41	California	36,880
42	Pennsylvania	35,240
43	District of Columbia	33,799
44	West Virginia	32,621
45	Virginia	31,296
46	Puerto Rico	28,987
47	Washington	25,996
48	Oregon	20,937
49	New Hampshire	20,292
50	Hawaii	13,267
51	Maine	10,752
52	Vermont	8,469

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	1,938
2	North Dakota	1,383
3	Kansas	1,346
4	Ohio	1,285
5	Connecticut	1,199
6	Idaho	1,142
7	Tennessee	1,056
8	Arizona	914
9	Wyoming	903
10	Nevada	856
11	Delaware	849
12	Indiana	833
13	New Mexico	772
14	South Dakota	765
15	Minnesota	759
16	Alaska	757
17	Utah	742
18	Nebraska	732
19	Michigan	724
20	Montana	722
21	California	719
22	Pennsylvania	711
23	Alabama	700
24	New Hampshire	691
25	Colorado	688
26	Illinois	659
27	West Virginia	644
28	Mississippi	643
29	Wisconsin	636
30	Arkansas	632
31	Kentucky	613
32	Massachusetts	607
33	Missouri	603
34	Iowa	590
35	Louisiana	558
36	Oklahoma	548
37	Washington	545
38	North Carolina	544
39	Georgia	508
40	New Jersey	506
41	New York	485
42	Texas	485
43	South Carolina	475
44	Virginia	471
45	Maryland	420
46	Florida	392
47	District of Columbia	339
48	Oregon	301
49	Maine	274
50	Puerto Rico	239
51	Vermont	144
52	Hawaii	48

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,974
2	New York	1,788
3	Massachusetts	1,620
4	Connecticut	1,482
5	Louisiana	1,437
6	North Dakota	1,418
7	Rhode Island	1,400
8	Mississippi	1,357
9	South Dakota	1,296
10	Illinois	1,153
11	Michigan	1,071
12	District of Columbia	997
13	Arizona	972
14	Indiana	966
15	Pennsylvania	924
16	Arkansas	923
17	Florida	906
18	Georgia	906
19	South Carolina	895
20	New Mexico	869
21	Iowa	863
22	Delaware	824
23	Maryland	820
24	Texas	818
25	Alabama	812
26	Nevada	777
27	Missouri	756
28	Tennessee	748
29	Minnesota	739
30	Montana	721
31	Wisconsin	712
32	Nebraska	674
33	Kansas	666
34	Colorado	637
35	Idaho	628
36	Ohio	614
37	North Carolina	543
38	Kentucky	528
39	California	517
40	Wyoming	516
41	West Virginia	502
42	Virginia	501
43	Oklahoma	491
44	New Hampshire	419
45	Washington	413
46	Puerto Rico	381
47	Utah	310
48	Oregon	263
49	Alaska	195
50	Hawaii	186
51	Maine	183
52	Vermont	137

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	27
2	Wyoming	24
3	Rhode Island	22
4	Kansas	17
5	Idaho	15
6	Nebraska	15
7	Arkansas	13
8	Colorado	13
9	Indiana	13
10	South Dakota	13
11	Connecticut	12
12	Illinois	12
13	Michigan	12
14	Missouri	11
15	New Mexico	11
16	Pennsylvania	11
17	West Virginia	11
18	Wisconsin	11
19	Montana	10
20	Tennessee	10
21	Kentucky	8
22	Mississippi	8
23	Nevada	8
24	New Jersey	8
25	Louisiana	7
26	Massachusetts	7
27	Minnesota	7
28	Alabama	6
29	Maryland	6
30	Ohio	6
31	Oregon	6
32	Texas	6
33	Arizona	5
34	Utah	5
35	California	4
36	Florida	4
37	Maine	4
38	New York	4
39	North Carolina	4
40	Oklahoma	4
41	Alaska	3
42	Delaware	3
43	District of Columbia	3
44	Georgia	3
45	Vermont	3
46	Virginia	3
47	Washington	3
48	Puerto Rico	2
49	South Carolina	2
50	New Hampshire	1
51	Hawaii	0
52	Iowa	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	224,550	1	99
Norton	Kansas	210,222	2	99
Bon Homme	South Dakota	204,608	3	99
Lincoln	Arkansas	201,474	4	99
Buffalo	South Dakota	200,815	5	99
Davidson	Tennessee	74,384	511	83
Richland	South Carolina	52,925	1371	56
York	South Carolina	40,441	2059	34
Orange	California	30,049	2528	19
Pierce	Washington	23,230	2765	11

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,363	3	99
Foster	North Dakota	5,919	4	99
Turner	South Dakota	5,606	5	99
Richland	South Carolina	746	1494	52
Davidson	Tennessee	654	1682	46
York	South Carolina	527	1944	38
Orange	California	514	1974	37
Pierce	Washington	382	2301	26

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.